Concept of Optimization Solver
For more information about the concept of Optimization Solver, see the video at the Bilibili website.
Operations teams routinely face problems with too many constraints, too many tradeoffs, and decisions that must be made faster than manual methods allow. The result is a plan that is reasonable but suboptimal.
Optimization Solver is a cloud-native mathematical optimization service that finds the best possible decision given your objectives and constraints. You define what you want to achieve and what limits apply—the solver finds the optimal answer.
What is mathematical optimization
Mathematical optimization finds the best solution to a problem from a defined set of possibilities, subject to constraints that reflect real-world limits.
Every optimization problem has three core elements:
Objective function: The goal to maximize (such as profit or throughput) or minimize (such as cost, time, or waste).
Decision variables: The choices the solver controls to influence the outcome—for example, which orders to assign to which warehouse, or how many units to produce per shift.
Constraints: The rules and limits the solution must satisfy, such as capacity limits, budget ceilings, regulatory requirements, or service-level agreements.
Given these three elements, the solver searches the solution space and returns the decision that best satisfies your objective without violating any constraint.
When to use Optimization Solver
Optimization Solver fits problems where:
Many decisions interact and affect each other simultaneously
Business rules and constraints are numerous or complex
The cost of a suboptimal decision is measurable and significant
Plans must be recalculated frequently in response to changing data
Common signals that optimization is the right tool include frequent replanning, exceptions that are hard to explain, or plans that consistently require manual override.
Heuristics and rule-based systems work well for simple, stable problems. When tradeoffs are expensive and constraints are complex, mathematical optimization finds provably better solutions.
Use cases
Optimization Solver is used across industries where complex, constrained decisions are made repeatedly:
Logistics and distribution: Assign orders to warehouses and carriers while satisfying lane capacities, delivery cutoff times, and service-level targets.
Workforce scheduling: Build shift schedules that meet labor regulations, skill requirements, availability constraints, and fairness rules across large teams.
Manufacturing and production: Sequence jobs and size production lots to minimize changeover costs, respect material availability, and meet due dates.
Supply chain planning: Allocate inventory across a network of facilities to balance demand coverage, holding costs, and replenishment lead times.
Energy and utilities: Dispatch generation assets to minimize fuel cost and emissions while meeting grid demand and reserve requirements.
Finance and portfolio management: Select assets and set weights to maximize expected return subject to risk limits, regulatory constraints, and liquidity requirements.
How Optimization Solver works
Define your problem as an optimization model: set the objective, declare decision variables, and specify constraints.
Submit the model to Optimization Solver through the API or console.
The solver applies algorithms—including linear programming (LP), mixed-integer linear programming (MILP), and quadratic programming (QP)—to search for the optimal solution.
The solver returns the optimal or best-found solution, along with solution quality metrics and status information.
For large or complex problems, Optimization Solver supports asynchronous job submission so your application does not block while the solve runs.